T Cells + Metabolism of CD4 Tonic TCR Signaling Inversely Regulates the Basal M. Allen Paul Sergushichev, David L. Donermeyer, Wing Y. Lam, Erika L. Pearce, Maxim N. Artyomov and Ashley A. Viehmann Milam, Juliet M. Bartleson, Michael D. Buck, Chih-Hao Chang, Alexey http://www.immunohorizons.org/content/4/8/485 https://doi.org/10.4049/immunohorizons.2000055 doi: 2020, 4 (8) 485-497 ImmunoHorizons This information is current as of November 23, 2020. References http://www.immunohorizons.org/content/4/8/485.full#ref-list-1 , 17 of which you can access for free at: cites 50 articles This article Email Alerts http://www.immunohorizons.org/alerts Receive free email-alerts when new articles cite this article. Sign up at: ISSN 2573-7732. All rights reserved. 1451 Rockville Pike, Suite 650, Rockville, MD 20852 The American Association of Immunologists, Inc., is an open access journal published by ImmunoHorizons by guest on November 23, 2020 http://www.immunohorizons.org/ Downloaded from by guest on November 23, 2020 http://www.immunohorizons.org/ Downloaded from
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T Cells+Metabolism of CD4Tonic TCR Signaling Inversely Regulates the Basal
M. AllenPaulSergushichev, David L. Donermeyer, Wing Y. Lam, Erika L. Pearce, Maxim N. Artyomov and
Ashley A. Viehmann Milam, Juliet M. Bartleson, Michael D. Buck, Chih-Hao Chang, Alexey
Tonic TCR Signaling Inversely Regulates the Basal Metabolismof CD4+ T Cells
Ashley A. Viehmann Milam,*,1 Juliet M. Bartleson,*,1 Michael D. Buck,†,‡ Chih-Hao Chang,§ Alexey Sergushichev,{
David L. Donermeyer,* Wing Y. Lam,k Erika L. Pearce,† Maxim N. Artyomov,* and Paul M. Allen**Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110; †Max
Planck Institute of Immunobiology and Epigenetics, Freiburg 79108, Germany; ‡The Francis Crick Institute, London NW1 1AT, United Kingdom;§The Jackson Laboratory, Bar Harbor, ME 04609; {ITMO University, St. Petersburg, Russia 197101; and kAmgen Research, Amgen, Inc., South San
Francisco, CA 94080
ABSTRACT
The contribution of self-peptide–MHC signaling in CD4+ T cells to metabolic programming has not been definitively established. In
this study, we employed LLO118 and LLO56, two TCRtg CD4+ T cells that recognize the same Listeria epitope. We previously have
shown that LLO56 T cells are highly self-reactive and respond poorly in a primary infection, whereas LLO118 cells, which are less self-
reactive, respond well during primary infection. We performed metabolic profiling and found that naive LLO118 had a dramatically
higher basal respiration rate, a higher maximal respiration rate, and a higher glycolytic rate relative to LLO56. The LLO118 cells also
exhibited a greater uptake of 2-NBD–glucose, in vitro and in vivo. We extended the correlation of low self-reactivity (CD5lo) with high
basal metabolism using two other CD4+ TCRtg cells with known differences in self-reactivity, AND and Marilyn. We hypothesized that
the decreased metabolism resulting from a strong interaction with self was mediated through TCR signaling. We then used an
inducible knock-in mouse expressing the Scn5a voltage-gated sodium channel. This channel, when expressed in peripheral T cells,
enhanced basal TCR-mediated signaling, resulting in decreased respiration and glycolysis, supporting our hypothesis. Genes and
metabolites analysis of LLO118 and LLO56 T cells revealed significant differences in their metabolic pathways, including the glycerol
phosphate shuttle. Inhibition of this pathway reverts the metabolic state of the LLO118 cells to be more LLO56 like. Overall, these
studies highlight the critical relationship between peripheral TCR–self-pMHC interaction, metabolism, and the immune response to
infection. ImmunoHorizons, 2020, 4: 485–497.
Received for publication June 22, 2020. Accepted for publication July 23, 2020.
Address correspondence and reprint requests to: Dr. Paul M. Allen, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8118, St. Louis,MO 63110. E-mail address: [email protected]
The microarray data presented in this article have been submitted to the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under accession numberGSE146069.
This work was supported by National Institutes of Health Grants AI138393 (to J.M.B.), CA034196 (to C.-H.C.), CA181125 (to E.L.P.), AI125618 (to M.N.A.), and AI139540 (toP.M.A.) and European Molecular Biology Organization Fellowship 1096-2018 (to M.D.B.).
A.A.V.M. and J.M.B. designed and performed experiments, analyzed data, and wrote the manuscript, M.D.B., C.-H.C., D.L.D., A.S., and W.Y.L. performed experiments,analyzed data, and generated figures, M.N.A. analyzed data, E.L.P. designed experiments, and P.M.A. designed experiments and wrote the manuscript.
Abbreviations used in this article: DN, double-negative; DP, double-positive; ECAR, extracellular acidification rate; FCCP, carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone; GAM, Genes and Metabolites; 2-NBDG, 2-(N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino)-2-deoxyglucose; OCR, oxygen consumption rate;OXPHOS, oxidative phosphorylation; SRC, spare respiratory capacity.
This article is distributed under the terms of the CC BY-NC 4.0 Unported license.
Initialmetabolic analysesofTcells focusedondifferencesbetweenresting and activated cells. These studies established that naiveT cells mainly use oxidative phosphorylation (OXPHOS) togenerate the relatively low bioenergetic needs required by theirquiescent state, whereas activated T cells shift their metabolismfrom OXPHOS to aerobic glycolysis to support the anabolicreactions demanded by clonal expansion and effector differenti-ation (1, 2).However,metabolic networks influenceT cell functionbeyond simply meeting the energy demands of the T cell. Amultitudeof signalingpathways essential for variousTcell functionsare closely interconnected with metabolic programming, eitherthroughsharedsignalingcomponentsordirectly throughmetaboliteregulation(3). In the lastdecade,complexroles fordistinctmetabolicprogramming in nearly every facet of the T cell response to Ag havebeen further elucidated, encompassing expansion, differentiation,effector function, and memory cell formation and maintenance (3).Conversely, although there has been some work interrogating themetabolic intricacies of naive, quiescent T cells, this remains arelatively underappreciated area of T cell biology.
The dampening of metabolic activity in quiescent T cells is anactive process (4). Studies have unveiled a critical role formTOR1inhibitors (i.e., TSC1, PTEN, and LKB1) in the enforcement of aquiescent program in naive, peripheral T cells. These moleculeshavebeen shown tohavedistinct effects on the regulationofT cellhomeostasis; interestingly, their deletion also uniquely modifieshow T cells respond to stimulation (5–7). This highlights thepossibility that metabolic networks may prime naive T cells totune their eventual response toAg, leadingus toquestionwhetherthere is metabolic heterogeneity within the naive CD4+ T cellpopulation.
Activation of CD4+ T cells involves the integration of multiplevariables: TCR signaling, costimulation, and cytokine instruction.All three of these activating components have been shown to affectthemetabolism of CD4+ T cells. In a naive state, cytokine signalingand TCR signaling, through tonic self-pMHC interactions, alsooccur. IL-7R signaling has been previously shown to affect naiveT cell metabolism, but this was considered in an all-or-nonemanner in which no IL-7R signaling lead to the death of naiveT cells because of an inability to meet their quiescent bioenergeticneeds (8); therefore, we wanted to determine whether subtledifferences in tonic TCR signaling could generate a metabolicallyheterogenous pool of naive CD4+ T cells.
Peripheral TCR–self-pMHC interactions are distinct fromthymic–self-pMHC interactions, as they do not induce the samesignals required for positive selection. Instead, peripheral tonicTCR signaling involves low-level stimulation that does notpropagate canonical activation pathways, but rather generatesnuanced effects on the activation state of the T cell and geneexpression levels (9, 10). There is a wide range of tonic signalingstrengths in the naive polyclonal T cell population, as determinedby Nur77 (11, 12) and CD5 (13) expression. This implies thatinteractionsof an individualTCRwith a specific self-pMHCligandcontrols the level of tonic signaling on a cellular basis.
Several studies involving blockade of TCR–self-pMHC inter-actions, either with anti–MHC class II Abs or genetic deletion ofMHCclass II onAPCs, have revealed the role tonic signaling playsin survival, homeostatic expansion, and Ag reactivity of CD4+
Tcells (14–24). Interestingly, tonic signalinghas alsobeen linked tometabolic activity inmemoryCD4+Tcells.Whendeprived of classII interactions, memory T cells responded poorly and hadindications of overall diminished metabolic activity relative toT cells that maintained TCR–self-pMHC interactions (14, 16, 19).The two primary indicators of tonic TCR signaling strength, CD5and Nur77, have also been shown to act as regulators of T cellmetabolism postactivation to alter Teff function in vivo (25–27).
To explore whether tonic TCR signaling could influence anaive T cell’s metabolic programming, we employed LLO56 andLLO118, two TCRtg CD4+ T cells that recognize the same Listeriamonocytogenes epitope with the same affinity, and which havebeenpreviously characterizedbyour laboratory (22,28, 29). In thisstudy, we show that naive LLO118 cells, which have low-tonicsignaling, exhibit heightened basal metabolic activity whencompared with their high-tonic signaling counterparts, LLO56.Specifically, metabolic profiling of the cells revealed that naiveLLO118 had a higher basal respiration rate, maximal respirationrate, and spare respiratory capacity (SRC) relative to LLO56.LLO118 cells also had a higher glycolytic rate and took up more2-NBD–glucose thanLLO56. Furthermore, the emergence of thesedifferences in LLO TCRtg metabolism coincided with the onset ofpositive selection in the developing thymocytes, underlining thedependence of naivemetabolic programming on TCR–self-pMHCinteractions.
Therefore, we hypothesized there was an inverse relationshipbetween the strengthof tonic signalingandbasalmetabolicactivityinnaiveCD4+T cells,whichwasmediated throughTCR signaling.We first confirmed that the metabolic phenotype observed in ourLLO TCRtg cells extended to other high and low-tonic signalingCD4+ T cells by interrogating the basal metabolism of another setof TCRtg T cells, as well as assessing glucose uptake in polyclonalCD4+ T cells. We then tested our hypothesis by using a Scn5a/CD4-cre mouse line, which we have previously demonstrated tohave polyclonal CD4+ T cells with more sensitive TCR proximalsignaling in response to peripheral self-pMHC(28).Themetabolicproperties of Scn5a-expressing naive CD4+ T cells were assessed,and we found that the increased sensitivity to self resulted indecreased basal and maximal respiration rates, supporting ourhypothesis. Collectively, these data establish a direct link betweentonic TCR signaling and basal metabolic activity in CD4+ T cells.
MATERIALS AND METHODS
MiceThe LLO56 and LLO118 TCR–transgenic lines, specific forlisteriolysin O (190–205) (LLO190–205/I-A
b), have been previouslydescribed (22, 28, 29). These mice were maintained on a Rag1-knockout backgroundwith homozygous congenicmarker expres-sion (LLO118-Ly5.1; LLO56-Thy1.1). The H-Y–specific Marilyn
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FIGURE 1. Naive LLO CD4+ T cells with low-tonic signaling exhibit a higher metabolism than those with high-tonic signaling.
(A) Enriched CD4+ T cells from spleens of LLO56 and LLO118 TCRtg mice were analyzed using a standard Seahorse protocol, with stepwise
injections of oligomycin, FCCP, and rotenone plus antimycin A. A total of 2 to 3 3 105 cells per well were used, with the cell counts always matched
between mice on individual runs, and a minimum of three wells per mouse were plated. The OCR, a readout of cellular respiration, was measured,
and a representative OCR curve is shown. (B) Basal OCR, the first OCR data point collected, was plotted for LLO56 and LLO118 pairs from individual
Seahorse runs (n = 10). Plot points represent averages of a minimum of three wells; p = 0.0009 by a paired t test. (C) Maximal OCR, (Continued)(Continued)
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TCR–transgenic line was acquired from National Institute ofAllergy and Infectious Diseases/Taconic, and the AND TCR–transgenic line (MCC-I-Ek–specific) and B6 mice were acquiredfromThe Jackson Laboratory. Derivation of the polyclonal Scn5a-transgenic mouse (and its subsequent crossing to a CD4-Cre line)has also been previously described (28). All mice were bredand housed in a specific pathogen-free facility at WashingtonUniversity, according to guidelines established by theWashingtonUniversity Division of Comparative Medicine.
Seahorse analysisOxygen consumption rate (OCR) and extracellular acidificationrate (ECAR) were measured using a 96-well XF extracellular fluxanalyzer (Seahorse Bioscience) and Extracellular Flux Assay Kit(XFe96; Agilent Seahorse). Assay setup has been previously de-scribed (30–33). Briefly, cells were plated in XF media supple-mented with glucose (25 mM), L-glutamine (2 mM), and sodiumpyruvate (1 mM). Measurements were taken at basal state, andafter the stepwise additionof oligomycin (1mM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP) (1.5 mM), androtenone (100 mM) plus antimycin A (1 mM). The mGPD2 specificinhibitor, iGP-1 (#5.30655.0001; Calbiochem/EMDMillipore) wasdissolved in DMSO and added to the extracellular flux analyses ateither 2.5 or 5.0 mM final concentration.
2-NBD–glucose uptakeTo measure in vitro 2-NBD–glucose uptake in T cells, RBC lysiswas performed on naive splenocytes. TCR-transgenic cells werefurther negatively bead enriched for CD4+ T cells (Mouse CD4+
T Cell Kit; Miltenyi Biotec), whereas polyclonal cells were notenriched.A total of 106CD4+Tcells orpolyclonal splenocyteswerethen plated in duplicate in media (RPMI 1640 with 10% FBS andLife Technologies Glutamax) supplemented with 50 mg/ml 2-(N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino)-2-deoxyglucose (2-NBDG)(Cayman Chemical), for 20 min at 37°C. Cells were then washedtwice in PBS and stained for FACS as described above. Only
CD4+TCRb+T cellswere included in analysis. For in vivo uptake,2-NBD–glucosewaspreparedat200mg/ml inPBSand injected i.p.at a dose of 1000mg/kg. Themicewere sacrificed after 15min, thelymphoid organs harvested and analyzed as described above.
Flow cytometryAll samples were analyzed on BD FACSCanto II or BDLSRFortessa cytometers, and data were analyzed using FlowJosoftware (FlowJo). The following Abs/clones were used for cellanalysis: CD3e (clone 145-2C11, FITC; BioLegend; clone 145-2C11,allophycocyanin; BioLegend), CD4 (clone RM4.5, FITC; Bio-Legend; clone RM4.5, eFluor 450; eBioscience; clone RM4.5,PerCP-Cy5.5; eBioscience), CD5 (clone 53-7.3, FITC; BD Biosci-ences), CD8a (clone 53-6.7, allophycocyanin; BD Biosciences),CD45.1/Ly5.1 (cloneA20, eFluor 450; eBioscience),CD90.1/Thy1.1(clone OX-7, PE; BioLegend), and TCRb (clone H57-597, PerCP-Cy5.5; BioLegend; clone H57-597, FITC; BD Biosciences).
Transcriptional profiling and genes andmetabolites analysisTranscriptional profiling ofnaive andD7 in vivo–activatedLLO118and LLO56 was performed using Affymetrix microarrays (MouseGene ST 1.0; Affymetrix) using standard Affymetric protocols.Naive LLO118 andLLO56Tcellswere purified from the spleens ofindividual mice (n = 4) from each of the TCRtg lines using aMiltenyi CD4+ Isolation Selection Kit (no. 130-104-454) followingthemanufacturer’s instructions.For theD7 invivo–activatedTcellisolation, 104of eitherLLO118orLLO56Tcellswere injected intoawild-type C57BL/6J mouse on day-1. On D0, they were infectedwith L. monocytogenes and the spleens from individual mice(LLO118 n = 4; LLO56 n = 3) were harvested on D7. The T cellswere purified by FACS on a BD FACSAria by sorting on CD4+ andthe appropriate congenic marker (Thy1.1 for LLO118 and Ly5.1 forLLO56). A dump gate of CD8, CD11b, CD11c, CD19, NK1.1, andMHC class II was used. RNA was purified from 1.5 to 2 3 106
T cells using an RNeasy Mini Kit (no. 74104; Qiagen), the cDNA
the first OCR data point collected after the injection of FCCP, was plotted from pairs from individual Seahorse runs (n = 10). Plot points represent
averages of a minimum of three wells; p = 0.0004 by a paired t test. (D) SRC was plotted from pairs from individual Seahorse runs (n = 10). SRC was
calculated by subtracting the basal OCR from the maximal OCR; p = 0.0176 by a paired t test. (E) Enriched CD4+ T cells from spleens of LLO56 and
LLO118 TCRtg mice were run using a standard Seahorse protocol, as described in (A). The ECAR, a readout of cellular glycolysis, was measured, and
a representative ECAR curve is shown. (F) Basal ECAR, the first ECAR data point collected, was plotted for LLO56 and LLO118 pairs from individual
Seahorse runs (n = 10). Plot points represent averages of a minimum of three wells; p = 0.0176 by a paired t test. (G) Maximal ECAR, the first ECAR
data point collected after the injection of FCCP, was plotted for LLO56 and LLO118 pairs from individual Seahorse runs (n = 10). Plot points represent
averages of a minimum of three wells; p = 0.0012 by a paired t test. (H) To determine the relative reliance of a CD4+ T cell population on cellular
respiration versus cellular glycolysis, the ratio of OCR/ECAR was calculated by dividing the maximal OCR by the maximal ECAR for each pair of
LLO56 and LLO118; p = 0.5313 by a paired t test. (I) Basal OCR of LLO56 and LLO118 T cells on D7 after in vivo activation by L. monocytogenes
infection as described in (B). The values represent the average of a minimum of three individual wells from individual mice from two independent
experiments; p = 0.0048 by an unpaired t test. (J) Maximal OCR of LLO56 and LLO118 T cells on D7 after in vivo activation by Listeria infection as
described in (C). The values represent the average of a minimum of three individual wells from individual mice from two independent experiments;
p = 0.0266 by an unpaired t test. (K) Basal ECAR of LLO56 and LLO118 T cells on D7 after in vivo activation by Listeria infection as described in (F). The values
represent the average of a minimum of three individual wells from individual mice from two independent experiments; p = 0.0065 by an unpaired t test. (L)
Maximal ECAR of LLO56 and LLO118 T cells on D7 after in vivo activation by Listeria infection as described in (G). The values represent the average of a
minimum of three individual wells from individual mice from two independent experiments; p = 0.0164 by an unpaired t test.
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FIGURE 2. Use of a second pair of low- and high-tonic signaling TCRtg CD4+ T cells, Marilyn and AND, respectively, further confirms that low-
tonic signaling correlates with increased basal metabolic activity.
(A) Mean CD5 levels are indicated for the four transgenic lines analyzed (LLO56, AND, LLO118, Marilyn), as well as a BL6 mouse (n = 2 mice each). (B)
Enriched CD4+ T cells from spleens of Marilyn and AND TCRtg mice were analyzed using a standard Seahorse protocol, as described in Fig. 1. A
representative OCR curve is shown. (C) Enriched CD4+ T cells from spleens of Marilyn and AND TCRtg mice were run using a standard Seahorse
protocol. A representative ECAR curve is shown. (D) Basal OCR, the first OCR data point collected, was plotted for Marilyn and AND (Continued)(Continued)
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prepared using a NuGen Pico SL Kit, and 5.5 mg of cDNA washybridized to the microarrays using standard Affymetric proto-cols. All data were normalized using RMA in Arraystar 12.Microarray data have been deposited in the Gene ExpressionOmnibus (https://www.ncbi.nlm.nih.gov/geo/) under accessionnumber GSE146069. Genes and metabolites analysis was per-formed comparing the naive LLO118 and LLO56 T cells and D7 invivo–activated LLO118 and LLO56 T cells using Genes andMetabolities (GAM) (https://artyomovlab.wustl.edu/shiny/gam/)on an atom-based network (34, 35).
Bacterial infectionsThe L. monocytogenes strain 1043S used in this study wasgenerously provided by D. Portnoy (University of California,Berkeley, CA), and L. monocytogenes infections of LLO56 andLLO118 mice were performed as previously described (28).
Statistical analysisPrism (versions 7 and 8) software for Mac OS X was used for allstatistical analysis. Statistical significance was determined usingeither a paired t test (for Seahorse runs), an unpaired t test, or anANOVA test, and a p value,0.05 was designated as the criterionfor significance.
RESULTS
In naive mice, CD4+ T cells with lower tonic signaling havehigher rates of respiration and glycolysis than CD4+ T cellswith higher tonic signalingTo compare the basal metabolic profiles of LLO56 and LLO118,splenocytes of naive, age-and sex-matched matched mice wereenriched for CD4+ T cells, and Seahorse platform analysis wasperformed on the enriched populations. The respiratory rate ofLLO118 cells was higher than that of LLO56 cells, as evidenced bythe higher OCR of these cells (Fig. 1A). Specifically, baselinerespiration was higher in naive LLO118 CD4+ T cells than in naiveLLO56 cells, as wasmaximal respiration, reached after addition ofthe uncoupling reagent FCCP (Fig. 1B, 1C). SRC, which has beendefined as a cell’s energy reserve capable of fueling cellularfunction above and beyond basic energy needs, was also higher inLLO118 than in LLO56 (36) (Fig. 1D). Likewise, glycolytic functionwas greater in LLO118 than in LLO56, as evidenced by the higherbasal ECAR and the higher maximal ECAR of LLO118 cells (Fig.1E–G). Interestingly, we found no difference in the ratio of OCR to
ECAR inLLO56andLLO118 cells, indicating that theyhave similarrelative reliance on respiration and glycolysis for meeting energyneeds (Fig. 1H). The same metabolic differences between LLO118and LLO56were observedwhen the LLOT cells were activated invivowithListeria and harvested on d7 postinfection, duringwhichLLO118TcellshadagreaterOCRandECARthanLLO56cells (Fig.1I–L). Overall, these findings indicate that the less self-reactiveLLO118 CD4+ T cell is more metabolically active in its naive statethan the more self-reactive LLO56 cell.
To determine whether the inverse correlation between tonicsignaling and basal metabolismwas specific to the LLO system orgeneralizable to other systems, we also interrogated the basalmetabolism of another set of CD4+ T cell transgenics, AND, andMarilyn. AND recognizes Moth cytochrome C (MCC)/I-Ek (37),whereasMarilyn recognizes the male Y Ag/I-Ab (38). Mandl et al.(13) examined a panel of TCRtg lines, including ANDandMarilyn,for their level of tonic signaling. Similar to LLO56, AND has high-tonic signaling and thus expresses high levels of CD5; like LLO118,Marilyn has lower tonic signaling and expresses lower levels ofCD5 (Fig. 2A).Mirroringourfindings in theLLOsystem, theCD5lo
Marilyn T cell had higher maximal respiration relative to AND(Fig. 2B, 2E)andSRC(Fig. 2F); however, therewasnot a significantdifference in the basal metabolism (Fig. 2D). Maximal glycolyticrates trended to be higher in Marilyn than in AND, but did notreach a level of significance (Fig. 2C, 2H). The ratio of OCR toECAR in AND and Marilyn T cells did not reach a level ofsignificance, indicating that they have similar relative reliance onrespiration and glycolysis for meeting energy needs (Fig. 2I).Collectively, these findings in the AND/Marilyn TCR systemsfurther supported our observation that cells with lower tonicsignaling have a higher metabolic capacity in their naive state.
Increased 2-NBD–glucose uptake correlates with the higherbasal metabolism in low-tonic signaling cellsBecause we observed enhanced glycolysis in LLO118 comparedwith LLO56, we also sought to determine if glucose uptake wasenhanced in these cells. To test this, wemeasured in vitro uptake of2-NBDG, a nonmetabolizable glucose analogue, in LLO56 andLLO118 T cells (39). Complementing our observation of higherglycolysis inLLO118 cells, 2-NBDGuptakewas approximately twiceashigh inLLO118cells as itwas inLLO56cells (Fig. 3A,3B). 2-NBDGuptake was also measured in vivo following i.p. injection, and weobserved higher 2-NBDG uptake in LLO118 cells, relative to LLO56cells (Fig. 3C). Similarly, 2NBDG uptake was also higher inMarilyncells relative to AND in vitro (Fig. 3D, 3E). We were further able to
pairs from individual Seahorse runs (n = 4). Plot points represent averages of a minimum of three wells; p = 0.0947 by a paired t test. (E) Maximal
OCR, the first OCR data point collected after the injection of FCCP, was plotted from Marilyn and AND pairs from individual Seahorse runs (n = 4).
Plot points represent averages of a minimum of three wells; p = 0.007 by a paired t test. (F) SRC was plotted from pairs of individual Seahorse runs.
SRC was calculated by subtracting the basal OCR from the maximal OCR. (G) Basal ECAR, the first ECAR data point collected, was plotted for Marilyn
and AND pairs from individual Seahorse runs (n = 4). Plot points represent averages of a minimum of three wells. (H) Maximal ECAR, the first ECAR
data point collected after the injection of FCCP, was plotted for Marilyn and AND pairs from individual Seahorse runs (n = 4). Plot points represent
averages of a minimum of three wells. (I) The ratio of OCR/ECAR was calculated by dividing the maximal OCR by the maximal ECAR for each pair
and the plot points represent averages of a minimum of three wells.
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extend this finding into polyclonal T cells, in which we observed aninverse correlation between CD5 levels and 2-NBDG uptake (Fig.3F–H). In C57BL/6J mice, peripheral CD4+ T cells with the lowest10% of CD5 expression in vitro took up significantly more 2-NBDGthanperipheralCD4+Tcellswith thehighest 10%ofCD5expression(Fig. 3F, 3H).A similardistinction in2-NBDGuptakebetweenCD5lo
and CD5hi was observed for CD8+ T cells (Fig. 3G, 3H). It should benoted that the CD5lo and CD5hi populations are not completelycomprised of native cells, but normal mice from our mouse colonyhave very low levels of activated T cells. Thus, the majority of theT cells we examined were naive. These findings show an inversecorrelation between strength of TCR–self-pMHC interactions andmetabolism existed for both CD4+ and CD8+ T cells.
To determine if we could identify the divergence of glucoseuptake in the developmental timeline of LLO56 and LLO118, wealso examined different populations of thymocytes in vitro. Wefound that indeveloping thymocytes,CD42CD82double-negative(DN) thymocytes from LLO56 and LLO118 take up similaramounts of 2-NBDG in vitro, as do CD4+CD8+ double-positive(DP) thymocytes. However, starting in positively selected DP (inthis study, defined as CD4+CD8+ thymocytes expressing very highlevels ofTCRb andCD5) andCD4 single-positive, 2-NBDGuptakein LLO56 decreases much more than the uptake of LLO118 (Fig.3I). As this observed change coincided with the onset of positiveselection (and thereforeTCRsignaling), it supports our hypothesisthat increased self-pMHC–TCR interaction in a CD4+ T cellpredisposes that cell to lower basal metabolism.
Peripheral CD4+ T cells with increased sensitivity toself-pMHC have decreased basal metabolismTo further elucidate the relationship between self-reactivity andmetabolism in polyclonal CD4+ T cells, we sought a method thatwould allow us to genetically control metabolism. To this end, weemployed a knock-in mouse line with inducible expression ofScn5a (sodium channel protein type five subunit a) (28). Scn5a isthe pore-forming component of a voltage-gated sodium channeltypically expressed in cardiac myocytes. We previously reportedthat when Scn5a is ectopically expressed in DP thymocytes, theyare endowedwith enhanced signaling to weak self-pMHC ligandsduring positive selection. Scn5a expression in CD4+ T cell hybridsequips them with increased sensitivity to self-pMHC, to the levelthat they are capable of responding to their positive-selectingpeptide (40). Recently, we demonstrated that expression of Scn5ain peripheral CD4+ T cells (using Scn5a+CD42Cre+ mice) resultedin increased proximal TCR signaling and increased peripheralCD5 expression and this expression of Scn5a in the LLO118T cells led to an impaired in vivo response to L. monocytogenes
CD5hi and CD5lo populations. (I) in vitro 2-NBDG uptake in naive thymocytes from either LLO56 (n = 4) or LLO118 (n = 4) mice was measured by
FACS after a 20-min incubation. Live/dead gating was used on single-cell suspensions, followed by doublet discrimination. Cells were then gated
based on CD4 and CD8 expression into DP (CD4+CD8+), DN (CD42CD82), and single-positive (SP; CD4+CD82) populations. Within the DP
population, another gate was drawn on TCRb++/CD5++ cells to interrogate DP cells initiating the process of positive selection. The 2-NBDG uptake
of each population is shown, and data are representative of three independent experiments.
FIGURE 4. Increasing TCR signaling results in correspondingly lower
respiration and glycolysis rates in naive CD4+ T cells.
(A) Enriched CD4+ T cells from spleens of Scn5+CD42cre+ mice and
Scn5+CD42cre2 mice were analyzed using a standard Seahorse pro-
tocol as described in Fig. 1. Shown is the OCR of three combined ex-
periments, with the points representing the mean 6 SEM. The p values
are shown for the basal (time 09) and maximal (time 429) values. (B)
Enriched CD4+ T cells from spleens of from Scn5+CD42cre+ mice and
Scn5+CD42cre2 paired mice were run using a standard Seahorse
protocol as described in Fig. 1. Shown is the ECAR of three combined
experiments, with the points representing the mean 6 SEM. The p
values are shown for the basal (time 09) and maximal (time 429) values.
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infection (28). To directly test the impact of increased sensitivityto self-pMHConmetabolism,weusedSeahorse platform analysisto compare Scn5a+CD42Cre+mice to Scn5a+CD42Cre–negativelittermates. Mirroring the differences we observed in LLO56and LLO118 basal metabolism, we found that increasing self-sensitivity in Scn5a+CD42Cre+ mice led to a compensatorydecrease in basal respiration (Fig. 4A), maximal respiration (Fig.4A), and basal ECAR (Fig. 4B) and maximal ECAR (Fig. 4B).These data indicate that increasing TCR signaling sensitivityin naive CD4+ T cells results in a compensatory decrease inmetabolism.
Identification of metabolic pathway differences between theLLO T cells in their activated statesTo gain unbiased insight into differences between LLO118 andLLO56 CD4+ T cells, we performed transcriptional profiling ofnaive LLO118 and LLO56 T cells and those isolated at D7postinfection. The naive cells had few transcriptional differences,withnoobvious candidates to explain their distinct characteristics.We thenanalyzedtheD7data in thecontext ofmetabolicnetworksby using GAM analysis (34). Using this innovative approach, wefound coordinated changes in a number of metabolic pathways(Fig. 5). Overall, LLO118 cells appeared to be metabolically much
more active than LLO56 cells, consistent with our Seahorseanalysis (Fig. 1). We found a metabolic bifurcation point thatdiscriminated between LLO118-like metabolism and LLO56-likemetabolism, centered at glycerol 3-phosphate (Fig. 5, inset). InLLO118T cells, thismetabolite can be directed toward the glycerolphosphate shuttle via the action of mitochondrial glycerol phos-phate dehydrogenase (mGpd2). In LLO56 T cells, glycerol3-phosphate is directed toward glycerolipid metabolism via theenzyme glycerophosphocholine phosphodiesterase 1 (Gpcpd1).The glycerol phosphate shuttle is a secondary mechanism thatallowsNADHgenerated in the cytosol byglycolysis to contributeto OXPHOS in the mitochondria, and thereby sustain ATPproduction (reviewed in Ref. 41). The rate-limiting enzyme forthe glycerol phosphate shuttle is mGPD2. mGPD2 is a mito-chondrial flavin-linked respiratory chain dehydrogenase thatoxidizes glycerol-3-phosphate to dihydroxyacetone phosphatewith concurrent reduction of FAD to FADH2 and transfer ofelectrons to CoQ (Fig. 5 inset) (41). Recently, the glycerolphosphate shuttle has been shown to regulate macrophageinflammatory responses (42) and promote skeletal muscleregeneration (43), supporting the concept that it could beinvolved in the observed metabolic differences betweenLLO118 and LLO56 T cells.
FIGURE 5. Metabolic pathway differences between LLO118 and LLO56 and identification of mGPD2 as a candidate gene.
Microarray data for LLO118 and LLO56 T cells activated in vivo by L. monocytogenes infection were analyzed on D7 post infection for metabolic
pathway differences using the GAM program, revealing significant differences between the two T cells. The pathways upregulated in LLO118 are
shown in blue, and those in LLO56 shown in red, with the intensity of the lines corresponding to the level of transcription. The enzymes involved in
each step of the pathways are shown. For clarity, the substrates and products have been omitted. The inset highlights a key branch point between
LLO118 and LLO56 T cells, highlighting mGPD2 as a candidate gene involved for the observed metabolic differences.
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Enhanced glycerol phosphate shuttle function contributesto basal metabolism of LLO118To obtain evidence in support of the glycerol phosphate shuttle inthe enhanced metabolism of LLO118 T cells, we targeted themGpd2 enzyme, using an available specific inhibitor, iGP-1 (44).iGP-1 specifically inhibits the enzymatic activity of mitochondrialGPD2 (mGPD2), but not the cytosolic GPD2 isoform (44). iGP-1 iscell permeable but not bioavailable in vivo. Treatment of theLLO118 T cells in vitro with the iGP-1 significantly reduced theirmetabolic OCR and ECAR (Fig. 6A–C). Treatment of the LLO56T cells with iGP-1 had a significant but weaker effect on theirmetabolism (Fig. 6A, 6D, 6E). This finding provides support forthe involvement of the glycerol phosphate shuttle in the increasedmetabolism of the LLO118 T cells.
DISCUSSION
Interactions between a T cell through its TCR and self-pMHC onAPCs are critical for the development and maintenance of anadaptive immune system. The role of self-pMHC in positiveselection of T cells in the thymus is well established (reviewed inRef. 45). TCR–self-pMHC interactions in the periphery areimportant for survival of T cells and initiation of activation. Inthis present study, we extended the role of these self-pMHCcomplexes to being involved in setting the basal metabolism ofnaiveT cells. In a polyclonal T cell repertoire, there is a continuumof strengths of interactions with self-pMHC, and we have shownthat there is an inverse relationship between the strength of theseinteractions and basal metabolism. Thus, T cells with the weakerinteractions have a higher basal metabolism, and conversely thosewith stronger interactions have lower basal metabolisms. Wepropose that the reason for this tuning of the basal metabolism isthat it represents a homeostatic balance between activation andautoimmunity, and permits the full T cell repertoire to participatein immune responses. There is emerging evidence that there arefunctional differences in CD4+ T cells with different strengths ofself-pMHC reactivity, whichwould support the notion that tuningof self-reactivity, metabolism, and T cell function allows the fullrepertoire to be involved in some aspect of an immune response(12, 13, 28, 29, 46). Other factors besides the level of self-reactivityof T cells can also be involve in regulating metabolism, such as thelevel of cytokine receptors. Anergic self-reactive T cells andT regulatory cells have been shown to have altered metabolismcomparted to naive cells (47), but the development of both of thesepopulations is still influenced by the strength of the TCR–self-pMHC interactions.
Through the analysis of transcriptional data by the GAMprogram (34), we identified the glycerol phosphate shuttle asonepotential keymetabolic pathwaydifference betweenT cellswith high and low basal metabolisms. Thus, cells with higherbasal metabolism require an increased glycerol phosphateshuttle function to provide NADH generated in the cytosol byglycolysis to contribute to OXPHOS in the mitochondria.Flavell and colleagues (48) have recently shown that themalate
aspartate shuttle, another mechanism through which cyto-plasmic NADH is shuttled to the mitochondria, was importantin T cell differentiation. The malate aspartate shuttle wasnecessary for the proliferation of Th cells, whereas, succinatedehydrogenase subsequently antagonized differentiation andenforced terminal effector function. We have identified theenzyme mitochondrial glycerol phosphate dehydrogenase(mGPD2) as a potential key enzyme in the enhanced metab-olism of T cells withweak self-pMHC interactions. mGPD2 hasbeen recently shown by Horng and colleagues (42) to be a keyregulator of glucose-oxidation in LPS-stimulatedmacrophagesin the optimal control of inflammatory gene induction andsuppression.
In these studies, we provide evidence of an inverse correlationbetween the level of self-reactivity and the metabolism of a T cell.There are some limitations from our experiments. It is currentlyvery difficult to enhance tonic signaling in T cells. We havedeveloped the Scn5a+model system, which does show an increasein the tonic signaling.Westill donotknowhowtheScn5a+voltage-gated sodium channel is enhancing tonic signaling, and how itrelates to the pathways normally involved in tonic signaling. Wefound increased uptake of the glucose analogue 2-NBD–glucose incells with low-tonic signaling. Future studies of glucosemetabolism in naive CD4+ T cells is difficult with currenttechniques because naive T cells have a low uptake of labeledcompounds needed to trace metabolic pathways and products(49). A recent promising report using a pulsed stable isotopelabeling by amino acids in cell culture approach, showed thatnaive T cells have a large number of stalled ribosomes, whichcan rapidly respond upon T cell activation (50). They did notexamine if naive T cells with high- and low-tonic signaling haddifferences in the number of stalled ribosomes in this initialreport. The implication ofmGPD2 in the enhancedmetabolismbetween the LLO56 and LLO118 cells was based on the GAManalysis and use of the iGP-1 inhibitor. Many in vitro metabolicinhibitors have shown off-target effects. Clearly definitivegenetic studies will have to be performed to establish the roleof this pathway in basal metabolism, with the caveat ofredundant metabolic pathways complicating the interpreta-tion. Because mGPD2 is expressed in multiple immune celltypes, a conditional knockout allele and a T cell–specific cre-deletermouse should address this issue. Another area of futureinvestigation will be to establish how signaling through theTCR via self-pMHC controls basal metabolism. Roose andcolleagues (46) have made the important finding that tonicmTORC1 signals in naive CD4+ T cells influence T cell fatedecisions and that theRas exchange factorRasgrp1 is necessary togenerate tonic mTORC1 signals. The well-established involve-ment of TORC1 in controlling cellularmetabolism thus provides apotential mechanism by which the strength of TCR–self-pMHCinteractions inversely regulate basal metabolism. Overall, ourfindings reveal an inverse relationship between the strength oftonic signaling in naive CD4+ T cells and their basal metabolism,with important implications for T cell activation, differentiation,and function.
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The authors have no financial conflicts of interest.
REFERENCES
1. Fox, C. J., P. S. Hammerman, and C. B. Thompson. 2005. Fuel feedsfunction: energy metabolism and the T-cell response. Nat. Rev.Immunol. 5: 844–852.
2. Jones, R. G., and C. B. Thompson. 2007. Revving the engine: signaltransduction fuels T cell activation. Immunity 27: 173–178.
3. Geltink, R. I. K., R. L. Kyle, and E. L. Pearce. 2018. Unraveling thecomplex interplay between T cell metabolism and function. Annu.Rev. Immunol. 36: 461–488.
4. Chapman, N. M., M. R. Boothby, and H. Chi. 2020. Metabolic co-ordination of T cell quiescence and activation. Nat. Rev. Immunol. 20:55–70.
5. Yang, K., G. Neale, D. R. Green, W. He, and H. Chi. 2011. The tumorsuppressor Tsc1 enforces quiescence of naive T cells to promoteimmune homeostasis and function. Nat. Immunol. 12: 888–897.
6. Liu, X., J. L. Karnell, B. Yin, R. Zhang, J. Zhang, P. Li, Y. Choi, J. S.Maltzman, W. S. Pear, C. H. Bassing, and L. A. Turka. 2010. Distinctroles for PTEN in prevention of T cell lymphoma and autoimmunityin mice. J. Clin. Invest. 120: 2497–2507.
7. MacIver, N. J., J. Blagih, D. C. Saucillo, L. Tonelli, T. Griss, J. C.Rathmell, and R. G. Jones. 2011. The liver kinase B1 is a central reg-ulator of T cell development, activation, and metabolism. J. Immunol.187: 4187–4198.
8. Rathmell, J. C., E. A. Farkash, W. Gao, and C. B. Thompson. 2001. IL-7enhances the survival and maintains the size of naive T cells. J.Immunol. 167: 6869–6876.
9. Guichard, V., N. Bonilla, A. Durand, A. Audemard-Verger, T. Guilbert,B. Martin, B. Lucas, and C. Auffray. 2017. Calcium-mediated shapingof naive CD4 T-cell phenotype and function. eLife 6: e27215.
10. Myers, D. R., J. Zikherman, and J. P. Roose. 2017. Tonic signals: whydo lymphocytes bother? Trends Immunol. 38: 844–857.
11. Ashouri, J. F., and A. Weiss. 2017. Endogenous Nur77 is a specificindicator of antigen receptor signaling in human T and B cells. J.Immunol. 198: 657–668.
12. Zinzow-Kramer, W. M., A. Weiss, and B. B. Au-Yeung. 2019. Adap-tation by naıve CD4+ T cells to self-antigen-dependent TCR signalinginduces functional heterogeneity and tolerance. Proc. Natl. Acad. Sci.USA 116: 15160–15169.
13. Mandl, J. N., J. P. Monteiro, N. Vrisekoop, and R. N. Germain. 2013.T cell-positive selection uses self-ligand binding strength to optimizerepertoire recognition of foreign antigens. Immunity 38: 263–274.
14. Bourgeois, C., G. Kassiotis, and B. Stockinger. 2005. A major role formemory CD4 T cells in the control of lymphopenia-induced pro-liferation of naive CD4 T cells. J. Immunol. 174: 5316–5323.
15. Brocker, T. 1997. Survival of mature CD4 T lymphocytes is dependenton major histocompatibility complex class II-expressing dendriticcells. J. Exp. Med. 186: 1223–1232.
16. De Riva, A., C. Bourgeois, G. Kassiotis, and B. Stockinger. 2007.Noncognate interaction with MHC class II molecules is essential formaintenance of T cell metabolism to establish optimal memory CD4T cell function. J. Immunol. 178: 5488–5495.
17. Hochweller, K., G. H. Wabnitz, Y. Samstag, J. Suffner, G. J.Hammerling, and N. Garbi. 2010. Dendritic cells control T cell tonicsignaling required for responsiveness to foreign antigen. Proc. Natl.Acad. Sci. USA 107: 5931–5936.
18. Jameson, S. C. 2002. Maintaining the norm: T-cell homeostasis. Nat.Rev. Immunol. 2: 547–556.
19. Kassiotis, G., S. Garcia, E. Simpson, and B. Stockinger. 2002. Im-pairment of immunological memory in the absence of MHC despitesurvival of memory T cells. Nat. Immunol. 3: 244–250.
20. Martin, B., C. Bécourt, B. Bienvenu, and B. Lucas. 2006. Self-recognition is crucial for maintaining the peripheral CD4+ T-cell poolin a nonlymphopenic environment. Blood 108: 270–277.
21. Martin, B., C. Bourgeois, N. Dautigny, and B. Lucas. 2003. On the roleof MHC class II molecules in the survival and lymphopenia-inducedproliferation of peripheral CD4+ T cells. Proc. Natl. Acad. Sci. USA100: 6021–6026.
22. Persaud, S. P., C. R. Parker, W. L. Lo, K. S. Weber, and P. M. Allen.2014. Intrinsic CD4+ T cell sensitivity and response to a pathogen areset and sustained by avidity for thymic and peripheral complexes ofself peptide and MHC. Nat. Immunol. 15: 266–274.
23. Stefanova, I., J. R. Dorfman, and R. N. Germain. 2002. Self-recognitionpromotes the foreign antigen sensitivity of naive T lymphocytes.Nature 420: 429–434.
24. Wulfing, C., C. Sumen, M. D. Sjaastad, L. C. Wu, M. L. Dustin, andM. M. Davis. 2002. Costimulation and endogenous MHC ligands con-tribute to T cell recognition. [Published erratum appears in 2002 Nat.Immunol. 3: 305.] Nat. Immunol. 3: 42–47.
25. Henderson, J. G., A. Opejin, A. Jones, C. Gross, and D. Hawiger. 2015.CD5 instructs extrathymic regulatory T cell development in responseto self and tolerizing antigens. Immunity 42: 471–483.
26. McGuire, D. J., A. L. Rowse, H. Li, B. J. Peng, C. M. Sestero, K. S.Cashman, P. De Sarno, and C. Raman. 2014. CD5 enhances Th17-celldifferentiation by regulating IFN-g response and RORgt localization.Eur. J. Immunol. 44: 1137–1142.
27. Liebmann, M., S. Hucke, K. Koch, M. Eschborn, J. Ghelman, A. I.Chasan, S. Glander, M. Schadlich, M. Kuhlencord, N. M. Daber, et al.2018. Nur77 serves as a molecular brake of the metabolic switchduring T cell activation to restrict autoimmunity. Proc. Natl. Acad. Sci.USA 115: E8017–E8026.
28. Milam, A. A. V., J. M. Bartleson, D. L. Donermeyer, S. Horvath,V. Durai, S. Raju, H. Yu, V. Redmann, B. Zinselmeyer, J. M. White, et al.2018. Tuning T cell signaling sensitivity alters the behavior of CD4+
T cells during an immune response. J. Immunol. 200: 3429–3437.29. Weber, K. S., Q. J. Li, S. P. Persaud, J. D. Campbell, M. M. Davis, and
P. M. Allen. 2012. Distinct CD4+ helper T cells involved in primaryand secondary responses to infection. Proc. Natl. Acad. Sci. USA 109:9511–9516.
30. Buck, M. D., D. O’Sullivan, and E. L. Pearce. 2015. T cell metabolismdrives immunity. J. Exp. Med. 212: 1345–1360.
31. Buck, M. D., D. O’Sullivan, R. I. Klein Geltink, J. D. Curtis, C. H.Chang, D. E. Sanin, J. Qiu, O. Kretz, D. Braas, G. J. W. van der Windt,et al. 2016. Mitochondrial dynamics controls T cell fate throughmetabolic programming. Cell 166: 63–76.
32. Chang, C. H., J. Qiu, D. O’Sullivan, M. D. Buck, T. Noguchi, J. D.Curtis, Q. Chen, M. Gindin, M. M. Gubin, G. J. W. van der Windt,et al. 2015. Metabolic competition in the tumor microenvironment is adriver of cancer progression. Cell 162: 1229–1241.
33. O’Sullivan, D., G. J. W. van der Windt, S. C.-C. Huang, J. D. Curtis,C. H. Chang, M. D. Buck, J. Qiu, A. M. Smith, W. Y. Lam, L. M. DiPlato,et al. 2014. Memory CD8(+) T cells use cell-intrinsic lipolysis tosupport the metabolic programming necessary for development.[Published erratum appears in 2018 Immunity 49: 375–376.] Immunity41: 75–88.
34. Sergushichev, A. A., A. A. Loboda, A. K. Jha, E. E. Vincent, E. M.Driggers, R. G. Jones, E. J. Pearce, and M. N. Artyomov. 2016. GAM: aweb-service for integrated transcriptional and metabolic networkanalysis. Nucleic Acids Res. 44: W194–W200.
35. Ulland, T. K., W. M. Song, S. C.-C. Huang, J. D. Ulrich, A. Sergushichev,W. L. Beatty, A. A. Loboda, Y. Zhou, N. J. Cairns, A. Kambal, et al. 2017.TREM2 maintains microglial metabolic fitness in Alzheimer’s disease.Cell 170: 649–663.e13.
https://doi.org/10.4049/immunohorizons.2000055
496 CD4+ T CELL TONIC SIGNALING REGULATES BASAL METABOLISM ImmunoHorizons
36. Yadava, N., and D. G. Nicholls. 2007. Spare respiratory capacity ratherthan oxidative stress regulates glutamate excitotoxicity after partialrespiratory inhibition of mitochondrial complex I with rotenone. J.Neurosci. 27: 7310–7317.
37. Kaye, J., M. L. Hsu, M. E. Sauron, S. C. Jameson, N. R. Gascoigne, andS. M. Hedrick. 1989. Selective development of CD4+ T cells intransgenic mice expressing a class II MHC-restricted antigen re-ceptor. Nature 341: 746–749.
38. Lantz, O., I. Grandjean, P. Matzinger, and J. P. Di Santo. 2000.Gamma chain required for naıve CD4+ T cell survival but not forantigen proliferation. Nat. Immunol. 1: 54–58.
39. Zou, C., Y. Wang, and Z. Shen. 2005. 2-NBDG as a fluorescent in-dicator for direct glucose uptake measurement. J. Biochem. Biophys.Methods 64: 207–215.
40. Lo, W. L., D. L. Donermeyer, and P. M. Allen. 2012. A voltage-gatedsodium channel is essential for the positive selection of CD4(+)T cells. Nat. Immunol. 13: 880–887.
41. Mracek, T., Z. Drahota, and J. Houstek. 2013. The function and therole of the mitochondrial glycerol-3-phosphate dehydrogenase inmammalian tissues. Biochim. Biophys. Acta 1827: 401–410.
42. Langston, P. K., A. Nambu, J. Jung, M. Shibata, H. I. Aksoylar, J. Lei,P. Xu, M. T. Doan, H. Jiang, M. R. MacArthur, et al. 2019. Glycerolphosphate shuttle enzyme GPD2 regulates macrophage inflammatoryresponses. [Published erratum appears in 2019 Nat. Immunol. 20:1555.] Nat. Immunol. 20: 1186–1195.
43. Liu, X., H. Qu, Y. Zheng, Q. Liao, L. Zhang, X. Liao, X. Xiong,Y. Wang, R. Zhang, H. Wang, et al. 2018. Mitochondrial glycerol 3-
44. Orr, A. L., D. Ashok, M. R. Sarantos, R. Ng, T. Shi, A. A. Gerencser,R. E. Hughes, and M. D. Brand. 2014. Novel inhibitors of mitochondrialsn-glycerol 3-phosphate dehydrogenase. PLoS One 9: e89938.
45. Klein, L., B. Kyewski, P. M. Allen, and K. A. Hogquist. 2014. Positiveand negative selection of the T cell repertoire: what thymocytes see(and don’t see). Nat. Rev. Immunol. 14: 377–391.
46. Myers, D. R., E. Norlin, Y. Vercoulen, and J. P. Roose. 2019. Activetonic mTORC1 signals shape baseline translation in naive T cells. CellRep. 27: 1858–1874.e6.
47. Zheng, Y., G. M. Delgoffe, C. F. Meyer, W. Chan, and J. D. Powell. 2009.Anergic T cells are metabolically anergic. J. Immunol. 183: 6095–6101.
48. Bailis, W., J. A. Shyer, J. Zhao, J. C. G. Canaveras, F. J. Al Khazal,R. Qu, H. R. Steach, P. Bielecki, O. Khan, R. Jackson, et al. 2019. Distinctmodes of mitochondrial metabolism uncouple T cell differentiation andfunction. [Published erratum appears in 2019 Nature 573: E2.] Nature571: 403–407.
49. Ma, E. H., M. J. Verway, R. M. Johnson, D. G. Roy, M. Steadman,S. Hayes, K. S. Williams, R. D. Sheldon, B. Samborska, P. A. Kosinski,et al. 2019. Metabolic profiling using stable isotope tracing revealsdistinct patterns of glucose utilization by physiologically activatedCD8+ T cells. Immunity 51: 856–870.e5.
50. Wolf, T., W. Jin, G. Zoppi, I. A. Vogel, M. Akhmedov, C. K. E. Bleck,T. Beltraminelli, J. C. Rieckmann, N. J. Ramirez, M. Benevento, et al.2020. Dynamics in protein translation sustaining T cell preparedness.Nat. Immunol. 21: 927–937.
https://doi.org/10.4049/immunohorizons.2000055
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